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Glama

Texas Open Data

by io.github.pipeworx-io

Recent Changes

recent_changes
Read-onlyIdempotent

"What's new with X" / "latest on Y" / "what happened to Z this week / month / quarter" / "updates on Acme" / "news on Tesla recently" / "what's happening with Apple" — change feed for a company in the last N days/weeks/months in ONE parallel call. Fans out to SEC EDGAR (filings since since), GDELT→GNews fallback (news mentions in window — GDELT preferred, GNews when rate-limited or 5xx), USPTO (patents granted; PatentsView API sunset May 2025 so this soft-fails until reactivated). since accepts ISO date ("2026-04-01") or relative shorthand ("7d", "30d", "3m", "1y"). Returns structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs. Use entity_profile instead when you want the static profile (filings + fundamentals + LEI + patents) regardless of window.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesEntity type. Only "company" supported today.
sinceYesWindow start — ISO date ("2026-04-01") or relative ("7d", "30d", "3m", "1y"). Use "30d" or "1m" for typical monitoring.
valueYesTicker (e.g., "AAPL") or zero-padded CIK (e.g., "0000320193").

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate readOnly, idempotent, not destructive. The description adds critical behavioral details: parallel call, source fan-out, fallback from GDELT to GNews, soft-fail for USPTO, return format with grouped changes and citation URIs. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured and concise (~150 words). It front-loads with intuitive query examples, then details sources, parameters, and return format. Every sentence adds value without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite no output schema, the description explains the return structure (structured changes[], total_changes, URIs). It covers all tool aspects: multiple sources, fallbacks, parameter behavior, and disambiguation from sibling. Complete for a complex tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema has 100% coverage, so baseline is 3. The description adds value by explaining `since` accepts ISO dates or relative shorthand (e.g., '7d', '3m'), gives typical usage '30d', and clarifies `value` can be ticker or CIK. This enhances understanding.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool provides a 'change feed for a company in the last N days/weeks/months' using multiple sources. It gives specific query examples and distinguishes from sibling entity_profile. The verb 'change feed' and resources (SEC EDGAR, GDELT, USPTO) are precise.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit usage examples and directly tells when to use an alternative: 'Use entity_profile instead when you want the static profile.' It also explains the `since` parameter format and fallback behavior, guiding correct invocation.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Texas Open Data toolsInspect
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TDQS

A3.8/5.0
Disambiguation3/5

Tools are individually well-described, but there is overlapping functionality among ask_pipeworx, ask_pipeworx_grounded, and deep_research, as well as multiple prediction market tools. The detailed descriptions help, but the number of similar tools creates some ambiguity.

Naming Consistency4/5

Tool names follow a consistent snake_case pattern with descriptive prefixes (e.g., ask_pipeworx, entity_profile, polymarket_edges). There are minor deviations like 'deep_research' vs 'bet_research' but overall the naming is predictable and clear.

Tool Count2/5

The server is named 'Texas Open Data' but only 3 of 33 tools (datasets, metadata, query) are directly related to Texas open data. The remaining tools cover a much broader domain (Pipeworx ecosystem), making the tool count inappropriate for the stated purpose.

Completeness2/5

For the stated purpose of Texas Open Data, the tool surface is incomplete—only basic query and metadata capabilities are provided, lacking data management, update, or delete operations. As a general pipeworx server it might be more complete, but the name suggests Texas-specific data.

Resources